Abstract

Recommender systems support users in finding items or users matching their individual preferences or interests. With the growing importance of social networks and the ubiquitous availability of internet connectivity, data streams become one of the most important information sources. Popular streamed data sources are micro blogging services (e.g. ``twitter''), update messages in social networks, or articles on online news portals. Traditional recommender algorithms focus on large user-item matrixes applying complex algorithms (e.g. "factorization machines") for extracting the dominant knowledge and reducing the noise. In stream-based scenarios these algorithms cannot be applied due to tight time-constraints and limited resources. In this paper we present a framework optimized for providing recommendations based on streams. We analyze the user-item interaction stream for several online news portals and present the computed characteristics of these streams. Subsequently, we develop several different algorithms optimized for providing recommendations based on streams fulfilling the requirements according to quality, robustness, scalability, and tight time-constraints. We evaluate the algorithms and combine different algorithms in ensembles in order to handle the context-dependent user expectations. The evaluation results show that the developed algorithms outperform traditional recommender approaches and allow us to provide context-aware relevant recommendations.

@inproceedings{
Lommatzsch:Real-timeRecommendationsForUser-ItemStreams,
author = {Andreas Lommatzsch and Sahin Albayrak},
title = {Real-time Recommendations for User-Item Streams},
booktitle = {Proc. of the 30th Symposium On Applied Computing, SAC 2015},
year = {2015},
series = {SAC '15},
pages = {1039--1046},
isbn = {978-1-4503-3196-8},
location = {Salamanca, Spain},
publisher = {ACM},
doi = {10.1145/2695664.2695678},
address = {New York, NY, USA},
keywords = {Recommender Systems, Streams, Ensembles, Semantic, News},
}